A knowledge manager at a legal services organization — I’ll call her Dana — pulled me into a consulting debrief in October with a screenshot she couldn’t explain. Her organization’s matter-type taxonomy, designed in 2021 with 40 carefully chosen terms aligned to their practice areas, now contained 1,703 terms. Nobody on her team had approved any of them. The SharePoint term store, configured to serve managed metadata columns across 14 document libraries and two matter intake forms, had been quietly accumulating entries for three years. Search relevance across their Elasticsearch index — which ingested SharePoint metadata via a Graph API…

… … …” } Where are literal escape sequences in the JSON string. I’ll write the whole html as one line with between block elements. One thing: should there be a newline after tags — yes, same separators. Also, JSON strings can’t contain raw newlines — I’ll make sure the html value is one continuous line in my output (the being two characters: backslash and n). Also check: any backslashes in content? No. Any double quotes? No — all attributes single-quoted, prose quotes are single quotes. Apostrophes fine. Em dashes (—) are fine as UTF-8. Let me also double check…

Enterprise knowledge management (KM) systems are supposed to make organizational knowledge findable, reusable, and governable. In practice, they often become expensive filing cabinets that nobody trusts. The failure pattern is not random. It repeats because organizations treat knowledge as a technology problem when it is actually a structural information problem. Taxonomy, metadata, findability, and governance are the load-bearing walls. When those are weak, the system collapses no matter how much is spent on the platform. This article is for the people who inherit a failed KM system or are asked to prevent the next one. It is written from the…

Enterprise knowledge management systems fail repeatedly for a structural reason: they are usually built as document repositories with a search box, not as governed information environments. The main entity here is the enterprise knowledge management system — the combination of content stores, metadata models, navigation structures, search configuration, and governance routines that an organization uses to make recorded knowledge findable and reusable. Adjacent concepts include taxonomy, metadata, findability, information architecture, content lifecycle, and knowledge governance. This matters to the iiminfo.org audience because most failed knowledge systems do not fail from bad software. They fail from unresolved structural decisions about how…

Enterprise knowledge management (KM) systems are the platforms, taxonomies, metadata schemas, and governance routines that organizations use to capture, organize, and retrieve institutional knowledge. They sit at the intersection of information architecture, records management, search engineering, and organizational behavior. When they fail, the cost is not just wasted software spend. It is the slow erosion of findability, the duplication of analytical work, and the quiet loss of institutional memory. This article examines the structural reasons these systems fail repeatedly, even when the technology is competent and the intent is sincere. The Failure Pattern Is Structural, Not Technological Most post-mortems of…

A senior infrastructure engineer retired from a mid-sized public utility. His replacement started the following Monday. Within two weeks, the new hire asked the question every new hire eventually asks: where are the design rationale documents for the SCADA migration project — the six years of trade-off analyses, vendor evaluation notes, and architectural decision records that should exist somewhere in the system? The documents did exist. All 247 of them. They were sitting in the document management system, exactly where the departing engineer had filed them: inside a folder structure gated by his role-group permissions. The replacement could not see…

Why Enterprise Knowledge Management Systems Fail Repeatedly By Rajiv Indrakanti Enterprise knowledge management systems fail when they treat knowledge as a container problem instead of a structure problem. The recurring pattern is not a lack of software features. It is a mismatch between how an organization names, classifies, and governs information and how people actually search for and use that information. In the language of this site, the failure is a structural information failure: taxonomies that do not reflect work, metadata that is incomplete or inconsistent, findability that depends on the searcher already knowing the answer, and governance that stops…

Enterprise knowledge management systems are the shared digital spaces where large organizations try to make internal information findable: intranets, document repositories, collaboration hubs, and search portals. They fail repeatedly because the underlying information architecture—taxonomy, metadata, and findability—gets treated as an afterthought rather than as the system’s operating logic. This article is for the people who inherit those failures: the records manager asked to fix a portal nobody uses, the IT director wondering why a second SharePoint migration produced the same complaints, and the information architect brought in after the third failed rollout. The pattern is familiar, but it is not…

I’ve watched the same cycle play out in more organizations than I care to remember. A leadership team, frustrated by scattered files and repeated mistakes, pours money into a shiny new knowledge management system. The rollout comes with town halls, training sessions, and big promises. For a few months, there’s a buzz. Then, slowly, the silence creeps back. The platform becomes a graveyard of outdated files and broken links. The search bar—the one tool everyone actually uses—returns results that are technically accurate but practically useless. The failure isn’t a crash. It’s a quiet, expensive fizzle. I’m Rajiv Indrakanti, and after…

I’ve seen the same story play out in over a dozen large organizations. A new knowledge management system is unveiled with great enthusiasm. Executives talk about breaking down silos, capturing institutional memory, and making every employee more effective. Then, about a year and a half later, the platform sits largely abandoned. Search delivers a jumble of irrelevant results. The taxonomy has become a tangled mess. And the people who were supposed to contribute have quietly gone back to emailing attachments and asking the person at the next desk. The failure of enterprise knowledge management isn’t really a technology problem. It’s…

The Anatomy of a Title That Breaks Search A knowledge manager at a mid-sized financial services firm — I’ll call them Meridian Trust — spent eighteen months watching a 4,000-article internal knowledge base become functionally unsearchable. The content wasn’t wrong. The authors weren’t careless people. The platform wasn’t broken. What had failed was something more fundamental and less visible: every title in the system was functioning as metadata, and nobody had treated it that way. Articles carried names like Q3 Process Update, Important: New Workflow, Policy Change — Please Read, and Re: Q2 Compliance Reminder. Each of these titles made…

Enterprise knowledge management (KM) systems have a way of crashing and burning, and most post-mortems miss the real culprit. The usual suspects get rounded up: low adoption, a culture that doesn’t share, weak executive backing. But after fifteen years of walking into organizations that just mothballed their second or third KM platform, I see a different pattern. The failure is structural. It lives in the taxonomy and metadata layer—the part everyone treats as an afterthought. When that layer breaks, findability collapses. Trust evaporates. The system turns into a write-only dumping ground. This article maps the exact failure points, why they…